Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Text2

Alerts

Air temperature [K] is highly overall correlated with Process temperature [K]High correlation
Process temperature [K] is highly overall correlated with Air temperature [K]High correlation
Rotational speed [rpm] is highly overall correlated with Torque [Nm]High correlation
Torque [Nm] is highly overall correlated with Rotational speed [rpm]High correlation
Machine failure is highly overall correlated with HDF and 2 other fieldsHigh correlation
HDF is highly overall correlated with Machine failureHigh correlation
PWF is highly overall correlated with Machine failureHigh correlation
OSF is highly overall correlated with Machine failureHigh correlation
RNF is highly skewed (γ1 = 22.87957015)Skewed
UDI is uniformly distributedUniform
UDI has unique valuesUnique
Product ID has unique valuesUnique
Tool wear [min] has 120 (1.2%) zerosZeros
Machine failure has 9661 (96.6%) zerosZeros
TWF has 9954 (99.5%) zerosZeros
HDF has 9885 (98.9%) zerosZeros
PWF has 9905 (99.1%) zerosZeros
OSF has 9902 (99.0%) zerosZeros
RNF has 9981 (99.8%) zerosZeros

Reproduction

Analysis started2023-07-05 08:43:19.892356
Analysis finished2023-07-05 08:43:28.261850
Duration8.37 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

UDI
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:28.298240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2023-07-05T10:43:28.362899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%
Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:28.485600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowM14860
2nd rowL47181
3rd rowL47182
4th rowL47183
5th rowL47184
ValueCountFrequency (%)
m14860 1
 
< 0.1%
m14868 1
 
< 0.1%
m14877 1
 
< 0.1%
l47182 1
 
< 0.1%
l47183 1
 
< 0.1%
l47184 1
 
< 0.1%
m14865 1
 
< 0.1%
l47186 1
 
< 0.1%
l47187 1
 
< 0.1%
m14869 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-07-05T10:43:28.661029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 8335
13.9%
L 6000
10.0%
4 5711
9.5%
1 5552
9.3%
2 5480
9.1%
3 4873
8.1%
9 4056
6.8%
8 4054
6.8%
7 4010
6.7%
6 3978
6.6%
Other values (3) 7951
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50000
83.3%
Uppercase Letter 10000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8335
16.7%
4 5711
11.4%
1 5552
11.1%
2 5480
11.0%
3 4873
9.7%
9 4056
8.1%
8 4054
8.1%
7 4010
8.0%
6 3978
8.0%
0 3951
7.9%
Uppercase Letter
ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
83.3%
Latin 10000
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 8335
16.7%
4 5711
11.4%
1 5552
11.1%
2 5480
11.0%
3 4873
9.7%
9 4056
8.1%
8 4054
8.1%
7 4010
8.0%
6 3978
8.0%
0 3951
7.9%
Latin
ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 8335
13.9%
L 6000
10.0%
4 5711
9.5%
1 5552
9.3%
2 5480
9.1%
3 4873
8.1%
9 4056
6.8%
8 4054
6.8%
7 4010
6.7%
6 3978
6.6%
Other values (3) 7951
13.3%

Type
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:28.717495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowL
3rd rowL
4th rowL
5th rowL
ValueCountFrequency (%)
l 6000
60.0%
m 2997
30.0%
h 1003
 
10.0%
2023-07-05T10:43:28.810153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 6000
60.0%
M 2997
30.0%
H 1003
 
10.0%

Air temperature [K]
Real number (ℝ)

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.00493
Minimum295.3
Maximum304.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:28.876503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum295.3
5-th percentile297.1
Q1298.3
median300.1
Q3301.5
95-th percentile303.5
Maximum304.5
Range9.2
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.0002587
Coefficient of variation (CV)0.0066674194
Kurtosis-0.83596167
Mean300.00493
Median Absolute Deviation (MAD)1.6
Skewness0.11427392
Sum3000049.3
Variance4.0010348
MonotonicityNot monotonic
2023-07-05T10:43:28.938970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.7 279
 
2.8%
298.9 231
 
2.3%
297.4 230
 
2.3%
300.5 229
 
2.3%
298.8 227
 
2.3%
300.6 216
 
2.2%
298.2 208
 
2.1%
302.3 203
 
2.0%
297.5 198
 
2.0%
300.4 198
 
2.0%
Other values (83) 7781
77.8%
ValueCountFrequency (%)
295.3 3
 
< 0.1%
295.4 3
 
< 0.1%
295.5 18
0.2%
295.6 38
0.4%
295.7 18
0.2%
295.8 19
0.2%
295.9 10
 
0.1%
296 6
 
0.1%
296.1 12
 
0.1%
296.2 26
0.3%
ValueCountFrequency (%)
304.5 1
 
< 0.1%
304.4 7
 
0.1%
304.3 15
 
0.1%
304.2 40
0.4%
304.1 46
0.5%
304 45
0.4%
303.9 58
0.6%
303.8 75
0.8%
303.7 96
1.0%
303.6 78
0.8%

Process temperature [K]
Real number (ℝ)

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.00556
Minimum305.7
Maximum313.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.003811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum305.7
5-th percentile307.7
Q1308.8
median310.1
Q3311.1
95-th percentile312.5
Maximum313.8
Range8.1
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.4837342
Coefficient of variation (CV)0.0047861536
Kurtosis-0.49973437
Mean310.00556
Median Absolute Deviation (MAD)1.1
Skewness0.015027268
Sum3100055.6
Variance2.2014672
MonotonicityNot monotonic
2023-07-05T10:43:29.071372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310.6 317
 
3.2%
310.8 273
 
2.7%
310.7 266
 
2.7%
308.6 265
 
2.6%
310.5 263
 
2.6%
310.1 260
 
2.6%
308.5 257
 
2.6%
310.4 254
 
2.5%
311 246
 
2.5%
310.9 245
 
2.5%
Other values (72) 7354
73.5%
ValueCountFrequency (%)
305.7 2
 
< 0.1%
305.8 3
 
< 0.1%
305.9 6
 
0.1%
306 14
0.1%
306.1 17
0.2%
306.2 26
0.3%
306.3 13
0.1%
306.4 8
 
0.1%
306.5 9
 
0.1%
306.6 13
0.1%
ValueCountFrequency (%)
313.8 2
 
< 0.1%
313.7 4
 
< 0.1%
313.6 16
 
0.2%
313.5 22
 
0.2%
313.4 24
0.2%
313.3 29
0.3%
313.2 50
0.5%
313.1 50
0.5%
313 55
0.5%
312.9 43
0.4%

Rotational speed [rpm]
Real number (ℝ)

Distinct941
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1538.7761
Minimum1168
Maximum2886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.139314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1168
5-th percentile1332
Q11423
median1503
Q31612
95-th percentile1868.05
Maximum2886
Range1718
Interquartile range (IQR)189

Descriptive statistics

Standard deviation179.2841
Coefficient of variation (CV)0.11651084
Kurtosis7.3929449
Mean1538.7761
Median Absolute Deviation (MAD)91
Skewness1.993171
Sum15387761
Variance32142.787
MonotonicityNot monotonic
2023-07-05T10:43:29.199790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1452 48
 
0.5%
1435 43
 
0.4%
1447 42
 
0.4%
1429 40
 
0.4%
1469 40
 
0.4%
1479 40
 
0.4%
1450 39
 
0.4%
1507 39
 
0.4%
1418 39
 
0.4%
1446 38
 
0.4%
Other values (931) 9592
95.9%
ValueCountFrequency (%)
1168 1
 
< 0.1%
1181 1
 
< 0.1%
1183 1
 
< 0.1%
1192 1
 
< 0.1%
1200 1
 
< 0.1%
1202 3
< 0.1%
1207 1
 
< 0.1%
1208 1
 
< 0.1%
1212 2
< 0.1%
1217 1
 
< 0.1%
ValueCountFrequency (%)
2886 1
< 0.1%
2874 1
< 0.1%
2861 1
< 0.1%
2833 1
< 0.1%
2825 1
< 0.1%
2760 1
< 0.1%
2737 1
< 0.1%
2721 1
< 0.1%
2710 1
< 0.1%
2709 1
< 0.1%

Torque [Nm]
Real number (ℝ)

Distinct577
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.98691
Minimum3.8
Maximum76.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.267157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile23.5
Q133.2
median40.1
Q346.8
95-th percentile56.1
Maximum76.6
Range72.8
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation9.9689337
Coefficient of variation (CV)0.24930493
Kurtosis-0.013240614
Mean39.98691
Median Absolute Deviation (MAD)6.8
Skewness-0.0095165958
Sum399869.1
Variance99.37964
MonotonicityNot monotonic
2023-07-05T10:43:29.330997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.2 52
 
0.5%
38.5 50
 
0.5%
42.4 50
 
0.5%
35.8 50
 
0.5%
37.7 49
 
0.5%
39.9 48
 
0.5%
40.6 48
 
0.5%
38.2 48
 
0.5%
40 47
 
0.5%
36.6 47
 
0.5%
Other values (567) 9511
95.1%
ValueCountFrequency (%)
3.8 1
< 0.1%
4.2 1
< 0.1%
4.6 1
< 0.1%
5.6 1
< 0.1%
5.8 1
< 0.1%
8 1
< 0.1%
8.8 1
< 0.1%
9.3 1
< 0.1%
9.7 2
< 0.1%
9.8 1
< 0.1%
ValueCountFrequency (%)
76.6 1
< 0.1%
76.2 1
< 0.1%
75.4 1
< 0.1%
74.5 1
< 0.1%
73.6 1
< 0.1%
72.8 1
< 0.1%
72 1
< 0.1%
71.8 1
< 0.1%
71.6 1
< 0.1%
71.3 1
< 0.1%

Tool wear [min]
Real number (ℝ)

Distinct246
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.951
Minimum0
Maximum253
Zeros120
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.397505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.95
Q153
median108
Q3162
95-th percentile206.05
Maximum253
Range253
Interquartile range (IQR)109

Descriptive statistics

Standard deviation63.654147
Coefficient of variation (CV)0.58965778
Kurtosis-1.1667371
Mean107.951
Median Absolute Deviation (MAD)55
Skewness0.027292239
Sum1079510
Variance4051.8504
MonotonicityNot monotonic
2023-07-05T10:43:29.463508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
1.2%
2 69
 
0.7%
5 63
 
0.6%
7 58
 
0.6%
59 58
 
0.6%
166 57
 
0.6%
119 57
 
0.6%
9 55
 
0.5%
146 54
 
0.5%
96 54
 
0.5%
Other values (236) 9355
93.5%
ValueCountFrequency (%)
0 120
1.2%
2 69
0.7%
3 34
 
0.3%
4 34
 
0.3%
5 63
0.6%
6 31
 
0.3%
7 58
0.6%
8 36
 
0.4%
9 55
0.5%
10 45
 
0.4%
ValueCountFrequency (%)
253 1
 
< 0.1%
251 1
 
< 0.1%
246 3
< 0.1%
244 3
< 0.1%
242 2
< 0.1%
241 1
 
< 0.1%
240 3
< 0.1%
239 1
 
< 0.1%
238 2
< 0.1%
237 1
 
< 0.1%

Machine failure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0339
Minimum0
Maximum1
Zeros9661
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.516190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18098084
Coefficient of variation (CV)5.3386679
Kurtosis24.546486
Mean0.0339
Median Absolute Deviation (MAD)0
Skewness5.1518518
Sum339
Variance0.032754065
MonotonicityNot monotonic
2023-07-05T10:43:29.561257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9661
96.6%
1 339
 
3.4%
ValueCountFrequency (%)
0 9661
96.6%
1 339
 
3.4%
ValueCountFrequency (%)
1 339
 
3.4%
0 9661
96.6%

TWF
Real number (ℝ)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046
Minimum0
Maximum1
Zeros9954
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.606440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06767051
Coefficient of variation (CV)14.71098
Kurtosis212.50276
Mean0.0046
Median Absolute Deviation (MAD)0
Skewness14.644462
Sum46
Variance0.0045792979
MonotonicityNot monotonic
2023-07-05T10:43:29.651568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9954
99.5%
1 46
 
0.5%
ValueCountFrequency (%)
0 9954
99.5%
1 46
 
0.5%
ValueCountFrequency (%)
1 46
 
0.5%
0 9954
99.5%

HDF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0115
Minimum0
Maximum1
Zeros9885
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.777851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10662498
Coefficient of variation (CV)9.2717376
Kurtosis82.009755
Mean0.0115
Median Absolute Deviation (MAD)0
Skewness9.1647887
Sum115
Variance0.011368887
MonotonicityNot monotonic
2023-07-05T10:43:29.822085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9885
98.9%
1 115
 
1.1%
ValueCountFrequency (%)
0 9885
98.9%
1 115
 
1.1%
ValueCountFrequency (%)
1 115
 
1.1%
0 9885
98.9%

PWF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0095
Minimum0
Maximum1
Zeros9905
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.868032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.097008716
Coefficient of variation (CV)10.211444
Kurtosis100.3235
Mean0.0095
Median Absolute Deviation (MAD)0
Skewness10.114516
Sum95
Variance0.0094106911
MonotonicityNot monotonic
2023-07-05T10:43:29.913573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9905
99.1%
1 95
 
0.9%
ValueCountFrequency (%)
0 9905
99.1%
1 95
 
0.9%
ValueCountFrequency (%)
1 95
 
0.9%
0 9905
99.1%

OSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0098
Minimum0
Maximum1
Zeros9902
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:29.958215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.098513606
Coefficient of variation (CV)10.052409
Kurtosis97.099856
Mean0.0098
Median Absolute Deviation (MAD)0
Skewness9.9539156
Sum98
Variance0.0097049305
MonotonicityNot monotonic
2023-07-05T10:43:30.005119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9902
99.0%
1 98
 
1.0%
ValueCountFrequency (%)
0 9902
99.0%
1 98
 
1.0%
ValueCountFrequency (%)
1 98
 
1.0%
0 9902
99.0%

RNF
Real number (ℝ)

SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019
Minimum0
Maximum1
Zeros9981
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2023-07-05T10:43:30.052182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.043549738
Coefficient of variation (CV)22.920915
Kurtosis521.57905
Mean0.0019
Median Absolute Deviation (MAD)0
Skewness22.87957
Sum19
Variance0.0018965797
MonotonicityNot monotonic
2023-07-05T10:43:30.103214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 9981
99.8%
1 19
 
0.2%
ValueCountFrequency (%)
0 9981
99.8%
1 19
 
0.2%
ValueCountFrequency (%)
1 19
 
0.2%
0 9981
99.8%

Interactions

2023-07-05T10:43:27.446554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.192541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.828566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.509651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.194023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.850638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.514781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.232665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.862044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.495183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.127085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.749947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.495914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.246911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.878365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.566486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.248505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.903300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.565689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.282538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.913370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.545322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.177674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.800707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.546475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.298328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.927682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.620734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.302450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.956550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.617713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.333600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.964302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.596378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.227446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.850492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.606928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.357694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.984117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.682190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.362746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.017807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.677703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.390666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.024712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.654300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.285148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.908313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.661467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.411689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.038988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.740360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.416857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.075670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.733006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.445424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.078930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.708437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.340041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.962610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.715576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.467893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.092998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.801226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.474276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.132585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.789789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.499974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.135401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.763301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.393734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.017994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.766543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.518640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.143573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.856784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.528027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.187524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.843236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.552848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.186713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.815538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.447313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.068797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.816840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.570978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.251928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.912950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.582248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.241644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.896518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.603734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.238903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.867531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.498021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.119670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.866127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.622723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.302590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.968692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.635861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.295409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.948980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.656070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.289537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.918958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.549178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.246607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.917166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.673864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.353616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.025255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.689031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.349216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.002363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.706158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.342044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.972027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.599092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.295646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.967205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.726195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.404590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.081165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.743259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.403584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.053457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.758014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.393280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.024612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.649918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.346467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:28.019126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:20.776819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:21.457994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.138119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:22.796449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:23.459367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.104540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:24.809416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:25.443437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.076435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:26.700159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-05T10:43:27.395499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-05T10:43:30.153153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
UDIAir temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Machine failureTWFHDFPWFOSFRNF
UDI1.0000.1130.324-0.0000.001-0.010-0.0230.009-0.022-0.024-0.001-0.006
Air temperature [K]0.1131.0000.8640.014-0.0120.0130.0830.0090.1380.0050.0020.019
Process temperature [K]0.3240.8641.0000.017-0.0140.0140.0400.0070.064-0.0030.0040.023
Rotational speed [rpm]-0.0000.0140.0171.000-0.9160.003-0.1670.006-0.161-0.036-0.134-0.012
Torque [Nm]0.001-0.012-0.014-0.9161.000-0.0040.169-0.0150.1390.0580.1540.014
Tool wear [min]-0.0100.0130.0140.003-0.0041.0000.1020.111-0.002-0.0090.1520.011
Machine failure-0.0230.0830.040-0.1670.1690.1021.0000.3630.5760.5230.5310.005
TWF0.0090.0090.0070.006-0.0150.1110.3631.000-0.0070.0090.0380.031
HDF-0.0220.1380.064-0.1610.139-0.0020.576-0.0071.0000.0180.046-0.005
PWF-0.0240.005-0.003-0.0360.058-0.0090.5230.0090.0181.0000.116-0.004
OSF-0.0010.0020.004-0.1340.1540.1520.5310.0380.0460.1161.000-0.004
RNF-0.0060.0190.023-0.0120.0140.0110.0050.031-0.005-0.004-0.0041.000

Missing values

2023-07-05T10:43:28.096192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-05T10:43:28.210855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UDIProduct IDTypeAir temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Machine failureTWFHDFPWFOSFRNF
01M14860M298.1308.6155142.80000000
12L47181L298.2308.7140846.33000000
23L47182L298.1308.5149849.45000000
34L47183L298.2308.6143339.57000000
45L47184L298.2308.7140840.09000000
56M14865M298.1308.6142541.911000000
67L47186L298.1308.6155842.414000000
78L47187L298.1308.6152740.216000000
89M14868M298.3308.7166728.618000000
910M14869M298.5309.0174128.021000000
UDIProduct IDTypeAir temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Machine failureTWFHDFPWFOSFRNF
99909991L57170L298.8308.5152736.23000000
99919992M24851M298.9308.4182726.15000000
99929993L57172L298.8308.4148439.28000000
99939994L57173L298.8308.4140147.310000000
99949995L57174L298.8308.3163427.912000000
99959996M24855M298.8308.4160429.514000000
99969997H39410H298.9308.4163231.817000000
99979998M24857M299.0308.6164533.422000000
99989999H39412H299.0308.7140848.525000000
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